US Presidential Election 2024, Results by state

This series of maps presents an early snapshot of the 2024 election, with most states reporting nearly complete vote counts by November 8. Each map provides a different view of the outcome. The conventional map uses colour to indicate state winners but can be misleading by overstating large, sparsely populated areas. The population-weighted cartogram resizes states based on population, highlighting the urban centres that often lean Democratic. Finally, a hexagon cartogram standardises states into hexagonal shapes proportional to their Electoral College votes, delivering a clear representation of the election result.
The population-weighted cartogram further uses colour gradients to depict vote shares across states, with the strongest victories for each candidate prominently marked. Notably, Maine and Nebraska stand out, given their unique system of splitting Electoral College votes. This map underscores growing political polarisation in the US: whereas many states had narrow margins in past elections, 2024 saw Trump win with a vote share of over 55% in 23 states, while Kamala Harris achieved similar dominance in just 13 states. Only 14 states had close contests, with Trump ultimately prevailing in most battleground states that usually determine the outcome of a presidential election in the US.
As county-level data emerges, the polarisation underlying this election will become even more pronounced, illustrating persistent social and political divisions. Despite efforts by Joe Biden during his presidency, these divides remain deep-seated. The implications of Trump’s return to office on this fragmented political landscape are still unfolding.
The hexagon cartogram arguably provides the most straightforward view of the Electoral College outcome, with each state represented by its electoral value and shaded for the winning candidate. This map also highlights states that shifted party allegiance since the 2020 election, offering a concise picture of the electoral majority that will see Trump assume the presidency once more.
This series of maps reveals the power of visualisation in interpreting election results. By approaching the outcome from multiple angles, these maps bring forward insights into voter density, geographic reach, and political power. Together, they reflect the complex contours of American democracy – a landscape defined by both vast geographic expanse and concentrated population centres. As Trump returns to the White House, the 2024 election maps remind us that the dynamics of US politics are anything but a simple divide between red and blue. These maps capture the underlying fabric of a deeply polarised nation, a story that will continue to shape American democracy in the years ahead.

US Presidential Election 2020, Results By County

The comparison of the electoral outcome in a normal map and an equal-population projection shows, how Biden’s vote dominates the spatial distribution of the votes in the most densely populated areas that stand out in the cartogram. Almost all large urban centres, including quite a few in the mid-west, show a majority of votes for Biden. Trump’s vote share was highest in the rural areas, while his former strong showing in some urban areas and especially suburban belts around the cities that he could win over in the 2016 election largely vanished.
However, while the county-level picture shows the most detailed picture of the political landscapes of the country, the actual results that matter for the outcome are those at state-level (which also led to Hillary Clinton losing the 2016 election despite having won the popular vote at the time). The following map series shows three different cartographic perspectives of the election outcome at state level: Shown here are one conventional map and two variations of cartogram depictions that demonstrate how the change in perspective provides unique new insights. The different visualizations show how using different base-maps can result in changing narratives for election outcomes. At the time of writing (Nov 16, 2020) no final results were fully declared, although most states had completed counting between 95 and 100 per cent of the votes. Therefore only minor changes in this overall picture provided here are to be expected (putting possible legal challenges to these outcomes aside).

US Presidential Election 202, Results by state

The upper two maps both use a gradual color scheme to highlight the different vote shares of the candidate with the respective highest vote share in a state (therefore being on track to winning all the state’s votes in the Electoral College). In the conventional map projection (top) this does resemble a significant dominance of the votes for the Republican Party and Donald Trump as the incumbent president across large parts of the country. Yet changing the base-map to a population-weighted cartogram (middle) where each state is proportional to the number of people who live there, this impression becomes relativized. The dominance of the Democratic Party with their candidate Joe Biden in some of the most populous states becomes apparent, while the Republican vote in the mid-western and central parts looks much less prevailing due to the respective small populations there.
The display of the vote shares also shows how politically divided the USA have become: in around 16 states the vote share of the winning candidate lies in the range of between 45 and 55 per cent, showing how relatively close the outcome has been. When the final results have been released, this pattern will become even more visible at larger scales. This will only be the start of trying to understand the full spatial patterns that defined this highly unusual presidential election. A geographical analysis will be a crucial part of identifying some of the underlying causes for such a polarized country. It will be crucial for the forthcoming presidency to understand these patterns when it comes to finding solutions to mending a highly divided society.
The final map (bottom) is the most accurate picture of the actual political outcome of the election. Here each hexagon represents a federal state. This was then resized according to the total number of votes that this state has in the Electoral College, the assembly which elects the next president. While the legal battle over the election outcome might continue for a while and could change some of the political landscapes drawn in these maps, it seems unlikely that this map is going to change significantly. What it shows is that the majority of electors in the Electoral College are mandated to vote for Joe Biden to become the 46th president of the United States of America.

This cartographic analysis provides an assessment of the impact of some key data of this election. The mapping techniques used in this analysis are deployed to create more comprehensive insights than conventional approaches usually provide. Apart from visualising the election statistics on a ‘normal’ geographic view that shows the country’s land area, two different variations of so-called cartograms are used. The second map type is a hexagonal cartogram where each hexagon shape represents one parliamentary constituency, therefore reflecting the seat distribution in the new parliament (for technical reasons, some minor changes in constituencies in recent years are reflected in split and merged hexagons). The third map type is a gridded population cartogram where each small area is shown proportional to its total population that lives there. While the electorate is a smaller fraction of the total population, this map gives a view of how the entire population in each area is represented through the respective political party that was elected there.
Although the two cartograms have a similar conceptual approach, their overall appearance and therefore their underlying message differs, not least because constituencies vary in size (by area and population) and therefore also in the number of people that they represent.
For the unfamiliar eye, cartograms are more difficult to read and interpret than the normal map. However, the normal map highly overrepresents the large rural areas and makes it difficult to understand the patterns in the most populated areas that have equal importance when it comes to understanding the political implications of an election outcome.
In this feature, all three map types are shown alongside each other for each variable mapped here. Shown is a selected range of key aspects that arise from the vote. This allows for a comparison of these through the different cartographic perspectives.

These maps showing the winning party in each constituency also include the changes that have occurred compared to the 2017 General Election. To account for the substantial changes in the last parliament with regards to members of parliament (MPs) changing their party affiliation (voluntarily or forced), the changes considered here reflect the most recent affiliation of an MP immediately before the election.

Although the political system in the UK is based on a first past the post representation, a look at the second placed candidates that stood for election is relevant. Some of the constituencies were won with a very small majority. 30 constituencies had a majority of under 1000 votes, another 32 were won with less than 2000 votes difference, which demonstrates what differences such small margins can make for the overall outcome in a first past the post system.
When viewing the maps of winners and second placed parties alongside each other, it becomes clear from the patchwork of colours shown here that despite a Conservative landslide win the country remains very much split politically.

The Conservative vote has spread widely at this election, as can be seen in more detail in the vote share maps included here for the Conservatives and Labour. Nevertheless, former Labour strongholds are still not completely in Conservative hands and the cartograms relativise the ‘disappearance’ of the Labour vote slightly in its magnitude.

Lastly, what was portrayed as election fatigue amongst voters has affected parties to different degrees. In this third general election within 4½ years, overall turnout went down by 1.5 per cent to 63.7 per cent of registered voters.

The spatial patterns of this are patchy and changes in turnout were perhaps not decisive, although it can be seen that in many seats won by the Liberal Democrats and the Scottish National Party turnout went up, while in many Conservative and Labour-won areas it was more likely to be down compared to the 2017 election.

These maps tell manifold stories of the 2019 election that are most likely to determine and shape the politics of the forthcoming years. How this matters geographically as indicated in this brief analysis is an important aspect in understanding the political implications of the election and – to stay with analogies from nature – the political storms that many commentators expect to follow.

CO₂ Emissions per capita 2016

A map of the global CO₂ emissions in 2016 provides an insight into the side of the ‘culprits’, who themselves can be viewed from different angles. The data shown in the first cartogram displays each country of the world resized by their total fossil CO₂ emissions (excluding effects of deforestation which accounts for 11 per cent of these emissions). Overlaid are the relative emissions in tons per capita in that year. By absolute quantity, China is the largest contributor, followed by the USA and India. The three countries account for approximately half the quantity of emissions in this dataset. At the same time these three countries differ significantly in their relative contribution: The USA is with 15.56 tons CO₂ per capita among the worst contributors, while China has approximately half (7.45) and India a fraction of that (1.92). These relative contributions are used as negotiating positions that emerging (as well as developing) countries need to increase emissions in order to achieve economic success.
Such patterns can be observed around the world, so that the maintaining or even increasing economic output – usually with increased emissions as a trade-off – lies in the national interest of many countries. For concerted action towards tackling climate change, the largest contributors would obviously have the biggest impact in reducing their emissions.
The political commitment to such changes has always been at the centre of global conventions on climate change that are adopted in the United Nations Framework Convention on Climate Change. The 1997 Kyoto Protocol was the first serious effort to not only acknowledging a role of humans in global warming, but to also implement measures to reducing this impact.

CO2 Emissions Changes 1990 to 2015

Nevertheless, ever since the agreement was signed, CO₂ emissions have been steadily rising, largely fuelled by considerable global economic growth. The second pair of cartograms show how individual countries have been contributing to these increases, and to a much smaller extent which countries managed a relative decline in that period.
The largest absolute CO2 emission increases between 1990 and 2015 came from emerging economies, most notably China, and to a smaller extent India, amongst other countries. Other regions, especially Eastern Europe, Central Asia, and Russia, have seen declines in the same period.
However, rather than these declines being due to radical political intervention or commitments of these countries, many of these changes can be attributed to rather indirect political changes. The fall of the iron curtain and a process of deindustrialisation of the most polluting industries there, therefore the results of a political and an economic transition.
What the data shown in these visualisations demonstrates is the lack of serious political will or incapability of the global community to genuine address human-made climate change at the level of international agreements. As this longer-term perspective demonstrates, this is not a phenomenon of recent years, rather than has been endemic ever since the Kyoto protocol. National economic interests have always undermined the small attempts of finding a joint global strategy. The more recent dominance of populist politics in those countries that are also major contributors to this problem will most likely make the quest for solutions through international agreements even more difficult. Whether this means that strategies for mitigating and containing the effects of anthropogenic climate change will become obsolete is still an unanswered question. But new solutions for adaptation to the new realities of a changed global climate seems inevitable at significant economic costs – which could become the price for a lack of global political leadership.

The cartogram shown in this graphic presents an experimental approach to the creation of a gridded world population cartogram. Cartograms have become a more commonly used alternative to conventional maps. Recent advances by computer scientists allow for more sophisticated analytical purposes (Tobler, 2004). These include the diffusion-based method for producing density-equalising maps by Gastner and Newman (2004). It is used here as the underlying basis for the development of gridded cartograms in which the cartogram transformation is based on an equally distributed grid (Hennig, 2013; Hennig et al, 2010).
The projection shown here is based on the transformation executed over the full extent the earth’s surface (including the oceans) using a 0.25° resolution grid. The resulting equal population cartogram has then been visualised showing the grid only over the land area. In addition, the outlines of the countries are shown. The graphic is available in colour online. The world’s and each region’s largest places (cities and countries) are labelled to enhance readability. The Prime Meridian and the Equator are also included as extremely distorted lines in the left and lower part of the map.
This almost egg-shaped representation shows the continents cramped together. Small parts of the sea areas are visible in this depiction as the white patches between the main continents, despite their population values being zero. This can be explained partly as a result of the density-equalising algorithm used to transform the maps. Most empty sea area is found at those locations where densely populated coastal areas are next to unpopulated sea area which, as an effect of the diffusion between highest and lowest densities, creates a uniform transition and equalises the differences between these two extremes.
Despite being an unusual form of a gridded cartogram, the analytical capabilities of the display provide insights into the human geography of the world’s population. The four traditional physical hemispheres, dividing the world into north, south, east, and west, become almost irrelevant and are replaced here by a new division. The population centre of the world is situated on top of a mountain next to the Tiger Lake (Badai Talai) near the city of Udaipur. That point, displayed in the map’s centre, stands symbolic for the effects of recent demographic changes in the world population.
The global population centre is gradually shifting from the currently most populated region in Asia towards a most rapidly growing African continent, which pushes the significance of Europe and the Americas literally towards the edges of the planet in this modern version of a mappa mundi.
Also labelled in the map are the cities of Chengdu and Chongqing in the (still) most populated country, China. The region surrounding them, situated in the west of the densely populated Eastern provinces (the Himalayan region in contrast is almost nonexistent in this map), is one of the fastest-growing megaurban regions on the planet, the real dimensions of which become graphically apparent only in visualisations such as this one.
More detailed interpretations of gridded cartograms can be gained by increasing the resolution of the underlying grid that is used in the transformation, as well as by changing the scale and areal extent at which the cartogram is generated [see the World Population Atlas map series for further examples].